Neural Networks (NN) are a wide class of flexible nonlinear regression and discriminant models, data reduction models, and nonlinear dynamical systems (Sarle, 1994). Considerable research has been done on the application of NN models for solving complex biological/medical problems related to pattern recognition, prediction, classification, and discrimination (e.g.,medical diagnosis, prediction of DNA and protein structure, survival analysis with censoring,etc.). The available literature suggests that NN models may have advantage in prediction over other statistical methods in highly nonlinear problems (Bing and Titterington; Ripley 1994; De Laurentis and Radvin, 1994; Doyle et al, 1995). We have initiated a study to evaluate the usefulness of NN models in the identification of factors associated with death and other serious adverse events in reports available in the VAERS database. A multilayer perceptron with one input layer, two hidden layers and one output layer has been used as the basic structure for this modelling approach (Dayhoff, 1990). As a starting point, we have taken 648 "serious" DTP reports with 5 input variables (age,sex,birth weight,vaccine dose,and vaccine type) and 4 output variables (death, disability,life-threatening, and extended stay in the hospital) from the VAERS database. Approximately half of the randomly selected cases were used to train the network. The remaining half were used to test the predictability of the NN model. Our preliminary results indicate that this model, with only two categories of outcome - death and non-death, can correctly predict outcome in 70 to 80 percent of all cases in the prediction subset of data. We are in the process of refining the model by adding additional input variables and examining several learning algorythms to increase the prediction, sensitivity, and specificity of the model